--- license: apache-2.0 pipeline_tag: text-generation library_name: transformers tags: - llm-as-judge - qwen2 --- # CompassJudger-2

## Introduction We introduce **CompassJudger-2**, a novel series of generalist judge models designed to overcome the narrow specialization and limited robustness of existing LLM-as-judge solutions. Current judge models often struggle with comprehensive evaluation, but CompassJudger-2 addresses these limitations with a powerful new training paradigm. Key contributions of our work include: - **Advanced Data Strategy:** We employ a task-driven, multi-domain data curation and synthesis strategy to enhance the model's robustness and domain adaptability. - **Verifiable Reward-Guided Training:** We supervise judgment tasks with verifiable rewards, guiding the model's intrinsic reasoning through chain-of-thought (CoT) and rejection sampling. A refined margin policy gradient loss further enhances performance. - **Superior Performance:** CompassJudger-2 achieves state-of-the-art results across multiple judge and reward benchmarks. Our 7B model demonstrates competitive accuracy with models that are significantly larger. - **JudgerBenchV2:** We introduce a new, comprehensive benchmark with 10,000 questions across 10 scenarios, using a Mixture-of-Judgers (MoJ) consensus for more reliable ground truth. This repository contains the **CompassJudger-2** series of models, fine-tuned on the Qwen2.5-Instruct series. ## Models | Model Name | Size | Base Model | Download | Notes | | :--------------------------------- | :--: | :------------------- | :----------------------------------------------------------: | :-------------------------------------------- | | 👉 **CompassJudger-2-7B-Instruct** | 7B | Qwen2.5-7B-Instruct | 🤗 [Model](https://huggingface.co/opencompass/CompassJudger-2-7B-Instruct) | Fine-tuned for generalist judge capabilities. | | 👉 **CompassJudger-2-32B-Instruct** | 32B | Qwen2.5-32B-Instruct | 🤗 [Model](https://huggingface.co/opencompass/CompassJudger-2-32B-Instruct) | A larger, more powerful judge model. | ## Quickstart Here is a simple example demonstrating how to load the model and use it for pairwise evaluation. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "opencompass/CompassJudger-2-7B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) # Example: Pair-wise Comparison prompt = """ Please act as an impartial judge to evaluate the responses provided by two AI assistants to the user question below. Your evaluation should focus on the following criteria: helpfulness, relevance, accuracy, depth, creativity, and level of detail. - Do not let the order of presentation, response length, or assistant names influence your judgment. - Base your decision solely on how well each response addresses the user’s question and adheres to the instructions. Your final reply must be structured in the following format: { "Choice": "[Model A or Model B]" } User Question: {question} Model A's Response: {answerA} Model B's Response: {answerB} Now it's your turn. Please provide selection result as required: """ messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=2048 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` ## Evaluation CompassJudger-2 sets a new state-of-the-art for judge models, outperforming general models, reward models, and other specialized judge models across a wide range of benchmarks. | Model | JudgerBench V2 | JudgeBench | RMB | RewardBench | Average | | :--------------------------------- | :------------: | :--------: | :-------: | :---------: | :-------: | | **7B Judge Models** | | | | | | | CompassJudger-1-7B-Instruct | 57.96 | 46.00 | 38.18 | 80.74 | 55.72 | | Con-J-7B-Instruct | 52.35 | 38.06 | 71.50 | 87.10 | 62.25 | | RISE-Judge-Qwen2.5-7B | 46.12 | 40.48 | 72.64 | 88.20 | 61.61 | | **CompassJudger-2-7B-Instruct** | **60.52** | **63.06** | **73.90** | **90.96** | **72.11** | | **32B+ Judge Models** | | | | | | | CompassJudger-1-32B-Instruct | 60.33 | 62.29 | 77.63 | 86.17 | 71.61 | | Skywork-Critic-Llama-3.1-70B | 52.41 | 50.65 | 65.50 | 93.30 | 65.47 | | RISE-Judge-Qwen2.5-32B | 56.42 | 63.87 | 73.70 | 92.70 | 71.67 | | **CompassJudger-2-32B-Instruct** | **62.21** | **65.48** | 72.98 | **92.62** | **73.32** | | **General Models (for reference)** | | | | | | | Qwen2.5-32B-Instruct | 62.97 | 59.84 | 74.99 | 85.61 | 70.85 | | DeepSeek-V3-0324 | 64.43 | 59.68 | 78.16 | 85.17 | 71.86 | | Qwen3-235B-A22B | 61.40 | 65.97 | 75.59 | 84.68 | 71.91 | For detailed benchmark performance and methodology, please refer to our [📑 Paper](https://huggingface.co/papers/2507.09104). ## License This project is licensed under the Apache 2.0 License. See the [LICENSE](https://github.com/open-compass/CompassJudger/blob/main/LICENSE) file for details. ## Citation If you find our work helpful, please consider citing our paper: ```bibtex @article{zhang2025compassjudger, title={CompassJudger-2: Towards Generalist Judge Model via Verifiable Rewards}, author={Zhang, Taolin and Cao, Maosong and Lam, Alexander and Zhang, Songyang and Chen, Kai}, journal={arXiv preprint arXiv:2507.09104}, year={2025} } ```